Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2018

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv contains a veritable treasure trove of learning methods you may use one day in the solution of data science problems. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a “thumbs up” icon. Consider that these are academic research papers, typically geared toward graduate students, post docs, and seasoned professionals. They generally contain a high degree of mathematics so be prepared. Enjoy!

Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. This paper by a team of NVIDIA AI researchers includes a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. The model outperforms other methods for irregular masks. A demo video is included below:

Automatic text summarization, the automated process of shortening a text while reserving the main ideas of the document(s), is a critical research area in natural language processing. The aim of this literature review is to survey the recent work on neural-based models in automatic text summarization. The author examines in detail ten state-of-the-art neural-based summarizers: five abstractive models and five extractive models. In addition, the paper discusses the related techniques that can be applied to the summarization tasks and present promising paths for future research in neural-based summarization.

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNN) in creating artistic imagery by separating and recombining image content and style. This process of using CNN to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. This review aims to provide an overview of the current progress towards NST, as well as discussing its various applications and open problems for future research.

Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. This paper introduces the building blocks for constructing spherical CNNs.

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. This paper proposes instead to directly target a later desired task by meta-learning an unsupervised learning rule, which leads to representations useful for that task.

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